Update app.py

app.py

@@ -19,8 +19,8 @@ def inference(img):
     return 'result/1.png'
 
 
-title = "Selective Residual M-Net (SRMNet)"
-description = "Gradio demo for SwinIR. SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See the paper and project page for detailed results below. Here, we provide a demo for real-world image SR.To use it, simply upload your image, or click one of the examples to load them."
+title = "Selective Residual M-Net for Real-world Image Denoising"
+description = "Gradio demo for SRMNet. SRMNet has competitive performance results on two synthetic and two realworld noisy datasets in terms of quantitative metrics and visual quality. See the paper and project page for detailed results below. Here, we provide a demo for real-world image denoising. To use it, simply upload your image, or click one of the examples to load them."
 article = "<p style='text-align: center'><a href='https://' target='_blank'>Selective Residual M-Net</a> | <a href='https://github.com/FanChiMao/SRMNet' target='_blank'>Github Repo</a></p>"
 
 examples = [['Noise.png']]